DOE OSTI2019
In last year’s Bison robustness report, the authors demonstrated that improving the Jacobian matrix can drastically increase solution convergence rates. The development of an automatic differentiation (AD) capability in the MOOSE framework has created the possibility of having exact Jacobian matrices for all of our material models. A large focus in this year’s report is the application of AD to MOOSE physics modules, such as the tensor mechanics module, and the application to Bison fuel and cladding material models. A second main focus in this year’s report is the investigation of a set of algorithmic solver changes to improve the robustness and performance of Bison LWR simulations. Beyond providing increased solver robustness, the ability to solve strongly coupled multiphysics problems, and solving large deformation problems with ease, these automatically generated perfect Jacobians also drastically reduce development time for new models. In models converted to AD, Jacobian computation made up the majority of the code base. With this code removed, both readability and maintainability of the MOOSE/Bison code base are improved. The work documented here significantly improved the robustness of the Bison LWR assessment case suite. Through algorithmic changes in solver infrastructure, such as the application of predictors and improvements in timestepping, tangible improvements in performance were observed. These improvements are documented using a new standard set of metrics that are collected in all assessment cases, and a suite of statistical analysis tools developed for this report.
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗